Paper Titled “Regimes Or Cycles In Tropical Cyclone Activity In The North Atlantic” By Aberson 2009

The January 2009 issue of the Bulletin of the American Meteorological Society had the very informative article

Aberson, Sim D.: 2009: Regimes or Cycles in Tropical Cyclone Activity in the North Atlantic.  Bulletin of the American Meteorological Society Volume 90, Issue 1 (January 2009) pp. 39–43 DOI: 10.1175/2008BAMS2549.1

The abstract reads

” A cautionary tale in which previously published results are shown to be invalid due to the
lack of statistical analyses in the original work.”

Text from this paper includes

“Kinsmen (1957) wrote, ‘the job of a scientist is to invent a story which accounts for a set of observations and then decide how likely the story is.’ In his 1957 work, ‘Proper and improper use of statistics in geophysics,’ he emphasized the role of the correct use of statistics in this decision. However, statistics
continue to be misused or altogether neglected in the refereed literature, with the inevitable result of misleading or erroneous conclusions.”

“Though the results of HW07 [Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the North Atlantic: Natural variability or climate trend? Philos. Trans. Roy. Soc. London, 365A, 2695–2716] are unlikely to be correct, this does not necessarily suggest that the alternative hypothesis—that the time series shows a cyclical pattern—is correct. The time series only has two full oscillations and is too short to test the likelihood that it is a cycle. Unfortunately, even if the data were completely accurate, decades may pass before the series is long enough to make any definitive statements on this topic. Nevertheless, the clear need for timely scientific results should not be a reason for shortcuts in the scientific process; correct statistical analyses must be performed to determine the likelihood that the hypothesis tested is valid.”

This paper informs us that i) natural variations in climate metrics are quite large and ii) the non-temporal homogeneity of the climate data can result in the misinterpretation of statistical results.

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